Digitally Reconstructed Radiographs (DRRs) are simulated two-dimensional (2D) X-ray or portal transmission images, which are computed from three-dimensional (3D) datasets such as computed tomography (CT), megavoltage computed tomography (MVCT), 3D imaging of high contrast objects using rotating C-arms, and the like. DRRs have many uses in the diagnosis, therapy and treatment workflows, such as in patient positioning for radiotherapy, augmented reality, and/or 2D to 3D registration between pre-surgical data and intra-surgical fluoroscopic images, for example.
DRRs are commonly generated by casting rays through the volumetric datasets and by integrating the intensity values along these rays, which is typically accomplished after passing the intensities through a lookup table that models ray-tissue interactions. Unfortunately, this process is prohibitively slow for real-time or near real-time applications.
Existing methods for DRR generation typically trade off processing speed versus accuracy, and/or require extensive pre-processing of the data. LaRose, for example, proposed an advanced DRR generation algorithm that also used hardware acceleration. Unfortunately, the LaRose algorithm had the drawback of using 2D texture-based volume rendering, which sacrificed accuracy.
Accordingly, what is needed is a system and method for flexible generation of digitally reconstructed radiographs. The present disclosure addresses these and other issues.